United States US: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.790 % in 2017. United States US: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.790 % from Dec 2017 (Median) to 2017, with 1 observations. United States US: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

It was estimated that as of 2023, around **** million people in the United States had been diagnosed with diabetes. The number of people diagnosed with diabetes in the U.S. has increased in recent years and the disease is now a major health issue. Diabetes is now the seventh leading cause of death in the United States, accounting for ******percent of all deaths. What is prediabetes? A person is considered to have prediabetes if their blood sugar levels are higher than normal but not high enough to be diagnosed with type 2 diabetes. As of 2021, it was estimated that around ** million men and ** million women in the United States had prediabetes. However, according to the CDC, around ** percent of these people do not know they have this condition. Not only does prediabetes increase the risk of developing type 2 diabetes, but also increases the risk of heart disease and stroke. The states with the highest share of adults who had ever been told they have prediabetes are California, Hawaii, and New Mexico. The prevalence of diabetes in the United States As of 2023, around *** percent of adults in the United States had been diagnosed with diabetes, an increase from ****percent in the year 2000. Diabetes is much more common among older adults, with around ** percent of those aged 60 years and older diagnosed with diabetes, compared to just ****percent of those aged 20 to 39 years. The states with the highest prevalence of diabetes among adults are West Virginia, Mississippi, and Louisiana, while Utah and Colorado report the lowest rates. In West Virginia, around ** percent of adults have been diagnosed with diabetes.

Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). Prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).

1) Data Introduction • The Diabetes Health Indicators Dataset is a large health dataset that collects various health indicators and lifestyle information related to diabetes diagnosis based on health surveys and medical records of the U.S. population.

2) Data Utilization (1) Diabetes Health Indicators Dataset has characteristics that: • The dataset consists of more than 250,000 samples and contains more than 20 health and demographic variables, including diabetes (binary or triage label), age, gender, BMI, blood pressure, cholesterol, smoking and drinking habits, physical activity, mental health, income, and education level. (2) Diabetes Health Indicators Dataset can be used to: • Diabetes prediction model development: It can be used to develop machine learning-based classification models that use health indicators and lifestyle data to predict the risk of developing diabetes. • A Study on the Correlation between Lifestyle and Diabetes: It can be used in epidemiological and public health studies to analyze the effects of various lifestyle and demographic variables such as smoking, drinking, exercise, and eating habits on diabetes incidence.

This dataset is from UCI machine learning repository: 130 US hospital diabetes dataset However, I did several cleaning and this is the output. How I did the cleaning, you can read more here https://github.com/rischanlab/Cleaning_diabetes_130_US_hospital_dataset

Source

https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008

The data are submitted on behalf of the Center for Clinical and Translational Research, Virginia Commonwealth University, a recipient of NIH CTSA grant UL1 TR00058 and a recipient of the CERNER data. John Clore (jclore '@' vcu.edu), Krzysztof J. Cios (kcios '@' vcu.edu), Jon DeShazo (jpdeshazo '@' vcu.edu), and Beata Strack (strackb '@' vcu.edu). This data is a de-identified abstract of the Health Facts database (Cerner Corporation, Kansas City, MO).

Context

The dataset represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria.

(1) It is an inpatient encounter (a hospital admission).

(2) It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis.

(3) The length of stay was at least 1 day and at most 14 days.

(4) Laboratory tests were performed during the encounter.

(5) Medications were administered during the encounter.

The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

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These data represent the predicted (modeled) prevalence of Diabetes among adults (Age 18+) for each census tract in Colorado. Diabetes is defined as ever being diagnosed with Diabetes by a doctor, nurse, or other health professional, and this definition does not include gestational, borderline, or pre-diabetes.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Age-adjusted rate of diabetes deaths by sex, race/ethnicity, age; trends if available. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau; 2010 Census, Tables PCT12, PCT12H, PCT12I, PCT12J, PCT12K, PCT12L, PCT12M; generated by Baath M.; using American FactFinder; Accessed June 20, 2017. METADATA:Notes (String): Lists table title, notes and sources.Year (Numeric): Year of data.Category (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only); age categories as follows: 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85+; United States.Rate per 100,000 people (Numeric): Rate of diabetes deaths. Rates for age groups are reported as age-specific rates per 100,000 people. All other rates are age-adjusted rates per 100,000 people.

According to the Juvenile Diabetes Research Foundation (JDRF), almost 1.25 million people in the United States (US) have type 1 diabetes, which makes them dependent on insulin injections. Nationwide, type 2 diabetes rates have nearly doubled in the past 20 years resulting in more than 29 million American adults with diabetes and another 86 million in a pre-diabetic state. The International Diabetes Federation (IDF)has estimated that there will be almost 650 million adult diabetic patients worldwide at the end of the next 20 years (excluding patients over the age of 80). At this time, pancreas transplantation is the only available cure for selected patients, but it is offered only to a small percentage of them due to organ shortage and the risks linked to immunosuppressive regimes. Currently, exogenous insulin therapy is still considered to be the gold standard when managing diabetes, though stem cell biology is recognized as one of the most promising strategies for restoring endocrine pancreatic function. However, many issues remain to be solved, and there are currently no recognized treatments for diabetes based on stem cells. In addition to stem cell research, severalβ-cell substitutive therapies have been explored in the recent era, including the use of acellular extracellular matrix scaffolding as a template for cellular seeding, thus providing an empty template to be repopulated with β-cells. Although this bioengineering approach still has to overcome important hurdles in regard to clinical application (including the origin of insulin producing cells as well as immune-related limitations), it could theoretically provide an inexhaustible source of bio-engineered pancreases

The Rio Grande Valley (RGV) in South Texas has one of the highest prevalence of obesity and type 2 diabetes (T2D) in the United States (US). We report for the first time the T2D prevalence in persons with HIV (PWH) in the RGV and the interrelationship between T2D, cardiometabolic risk factors, HIV-related indices, and antiretroviral therapies (ART). The PWH in this study received medical care at Valley AIDS Council (VAC) clinic sites located in Harlingen and McAllen, Texas. Henceforth, this cohort will be referred to as Valley AIDS Council Cohort (VACC). Cross-sectional analyses were conducted using retrospective data obtained from 1,827 registries. It included demographic and anthropometric variables, cardiometabolic traits, and HIV-related virological and immunological indices. For descriptive statistics, we used mean values of the quantitative variables from unbalanced visits across 20 months. Robust regression methods were used to determine the associations. For comparisons, we used cardiometabolic trait data obtained from HIV-uninfected San Antonio Mexican American Family Studies (SAMAFS; N = 2,498), and the Mexican American population in the National Health and Nutrition Examination Survey (HHANES; N = 5,989). The prevalence of T2D in VACC was 51% compared to 27% in SAMAFS and 19% in HHANES, respectively. The PWH with T2D in VACC were younger (4.7 years) and had lower BMI (BMI 2.43 units less) when compared to SAMAFS individuals. In contrast, VACC individuals had increased blood pressure and dyslipidemia. The increased T2D prevalence in VACC was independent of BMI. Within the VACC, ART was associated with viral load and CD4+ T cell counts but not with metabolic dysfunction. Notably, we found that individuals with any INSTI combination had higher T2D risk: OR 2.08 (95%CI 1.67, 2.6; p < 0.001). In summary, our results suggest that VACC individuals may develop T2D at younger ages independent of obesity. The high burden of T2D in these individuals necessitates rigorously designed longitudinal studies to draw potential causal inferences and develop better treatment regimens.

The SEARCH for Diabetes in Youth (SEARCH) study was initiated in 2000 to address major knowledge gaps in the understanding of childhood diabetes. The SEARCH study (SEARCH 1-3) was conducted at five sites across the U.S. and established a longitudinal cohort to assess the natural history and risk factors for acute and chronic diabetes-related complications as well as the quality of care and quality of life of persons with diabetes from diagnosis into young adulthood. The cohort study (SEARCH 4) was developed by recruiting incident cases in 2002 to 2006, 2008, and 2012 that had a baseline visit near diagnosis and at least 5 years of diabetes duration at the cohort visit assessment. In the first two phases of SEARCH (SEARCH 1 and 2), individuals newly diagnosed with diabetes in 2002– 2006 and 2008 were recruited for a baseline research visit. Incident cases from 2002–2005 were also asked to return for visits at 12, 24, and 60 months after their baseline visit to measure risk factors for diabetes complications. In the third phase (SEARCH 3), a subset of SEARCH participants with a duration of diabetes >5 years were recruited for an outcome visit between 2011 and 2015. In addition, individuals incident in 2012 were invited for a baseline visit. In the fourth phase (SEARCH 4), a subset of SEARCH participants aged >10 years with at least 5 years of diabetes duration were invited to another study visit between 2015 and 2019. Those invited to the in-person research visit included all individuals with type 2 diabetes, all non-Whites, and a random sample of non-Hispanic Whites with type 1 diabetes.

Note: Specimens are available from SEARCH 4 only.

Gross-Profit-Margin Time Series for Tandem Diabetes Care Inc. Tandem Diabetes Care, Inc. designs, develops, and commercializes technology solutions for people living with diabetes in the United States and internationally. The company's flagship product is the t:slim X2 insulin delivery system; and Tandem Mobi insulin pump, an automated insulin delivery system. It also sells single-use products, including cartridges for storing and delivering insulin, and infusion sets that connect the insulin pump to the user's body. In addition, the company offers Tandem Device Updater used to update the pump software from a personal computer; Tandem Source, a web-based data management platform, which provides a visual way to display diabetes therapy management data from the pumps, integrated CGMs; and Sugarmate, a mobile app used to help people visualize diabetes therapy data. It has collaboration agreement with the University of Virginia Center for Diabetes Technology for research and development of fully automated closed-loop insulin delivery systems. The company was formerly known as Phluid Inc. and changed its name to Tandem Diabetes Care, Inc. in January 2008. Tandem Diabetes Care, Inc. was incorporated in 2006 and is headquartered in San Diego, California.

Price-To-Tangible-Book-Ratio Time Series for Tandem Diabetes Care Inc. Tandem Diabetes Care, Inc. designs, develops, and commercializes technology solutions for people living with diabetes in the United States and internationally. The company's flagship product is the t:slim X2 insulin delivery system; and Tandem Mobi insulin pump, an automated insulin delivery system. It also sells single-use products, including cartridges for storing and delivering insulin, and infusion sets that connect the insulin pump to the user's body. In addition, the company offers Tandem Device Updater used to update the pump software from a personal computer; Tandem Source, a web-based data management platform, which provides a visual way to display diabetes therapy management data from the pumps, integrated CGMs; and Sugarmate, a mobile app used to help people visualize diabetes therapy data. It has collaboration agreement with the University of Virginia Center for Diabetes Technology for research and development of fully automated closed-loop insulin delivery systems. The company was formerly known as Phluid Inc. and changed its name to Tandem Diabetes Care, Inc. in January 2008. Tandem Diabetes Care, Inc. was incorporated in 2006 and is headquartered in San Diego, California.

Background: Type 2 diabetes rates in the general population have risen with the growing obesity epidemic. Knowledge of temporal patterns and factors associated with comorbid diabetes among stroke patients may enable health practitioners and policy makers to develop interventions aimed at reducing diabetes rates, which may consequently lead to declines in stroke incidence and improvements in stroke outcomes. Methods: Using the Nationwide Inpatient Sample (NIS), a nationally representative data set of US hospital admissions, we assessed trends in the proportion of acute ischemic stroke (AIS) patients with comorbid diabetes from 1997 to 2006. Independent factors associated with comorbid diabetes were evaluated using multivariable logistic regression. Results: Over the study period, the absolute number of AIS hospitalizations declined by 17% (from 489,766 in 1997 to 408,378 in 2006); however, the absolute number of AIS hospitalizations with comorbid type 2 diabetes rose by 27% [from 97,577 (20%) in 1997 to 124,244 (30%) in 2006, p < 0.001]. The rise in comorbid diabetes over time was more pronounced in patients who were relatively younger, Black or ‘other’ race, on Medicaid, or admitted to hospitals located in the South. Factors independently associated with higher odds of diabetes in AIS patients were Black or ‘other’ versus White race, congestive heart failure, peripheral vascular disease, history of myocardial infarction, renal disease and hypertension. Conclusions: Although hospitalizations for AIS in the US decreased from 1997 to 2006, there was a steep rise in the proportion with comorbid diabetes (from 1 in 5 to almost 1 in 3). Specific patient populations may be potential targets for mitigating this trend.

The share of the population with overweight in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 77.43 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Canada and Mexico.

www.insight.hdrhub.org/about-us

Background: Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Cardiovascuar disease is the most prevalent cause of morbidity and mortality in people with diabetes. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust cardiac outcome data.

Geography: The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data sources:
1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. 2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.

Scope: All Birmingham, Solihull and Black Country diabetic eye screened participants who have been admitted to UHB with a cardiac related health concern from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care hospital cardiac outcome data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to cardiac disease • Outcome

Website: https://www.retinalscreening.co.uk/

The North American self-monitoring blood glucose (SMBG) market, valued at $8.10 billion in 2025, is projected to experience robust growth, driven by the increasing prevalence of diabetes and the rising geriatric population. The market's Compound Annual Growth Rate (CAGR) of 6.98% from 2019 to 2025 indicates a consistent upward trend. Key drivers include advancements in SMBG device technology, such as the development of more accurate, user-friendly, and cost-effective glucometers and lancets. Furthermore, increased awareness of diabetes management and the growing adoption of remote patient monitoring contribute significantly to market expansion. While data for individual countries within North America (United States, Canada, and Rest of North America) are not provided, we can infer that the United States likely holds the largest market share given its substantial population and higher prevalence of diabetes compared to Canada. The Rest of North America segment will likely exhibit moderate growth, reflecting the combined market dynamics of smaller countries in the region. Market restraints may include the potential for reimbursement challenges and the emergence of alternative glucose monitoring technologies, such as continuous glucose monitors (CGMs). However, the overall market outlook remains positive, fueled by the persistent need for effective diabetes management. Major players like Abbott, Roche, and LifeScan continue to innovate and compete, further shaping the market landscape. The forecast period (2025-2033) promises continued growth driven by technological advancements and an aging population requiring consistent blood glucose monitoring. The competitive landscape is characterized by established players like Abbott, Roche, and LifeScan, along with other significant contributors like Arkray, Ascensia, and Agamatrix. These companies are continuously striving for innovation to maintain their market share. This involves introducing technologically advanced devices, improving user experience, and developing more comprehensive diabetes management solutions. The increasing demand for accurate, convenient, and affordable SMBG devices, coupled with the growing awareness of diabetes self-management, will drive further market expansion. The incorporation of data analytics and connectivity features in SMBG devices also plays a crucial role in fostering better diabetes management and patient outcomes, thereby bolstering the market's growth trajectory. The focus on improving patient outcomes and reducing healthcare costs will likely shape future market developments, attracting further investments and technological advancements within the sector. Recent developments include: May 2023: LifeScan announced positive data from a study of real-world evidence supporting its Bluetooth-connected blood glucose meter. Evidence from more than 55,000 people with diabetes demonstrated sustained improvements in readings in range. The analysis focuses on changes over 180 days. LifeScan published results in the peer-reviewed journal Diabetes Therapy. The company’s OneTouch Bluetooth-connected blood glucose meter and mobile diabetes app provide simplicity, accuracy, and trust., January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence from Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes is one of the largest combined blood glucose meter and mobile diabetes app datasets ever published.. Notable trends are: Blood Glucose Test Strips Held the Largest Market Share in Current Year.

Semi-structured interviews with 36 individuals with either type 1 or type 2 diabetes. The interviews address their diagnosis and current life with diabetes, their use of Facebook and consumption and production of online content in relation to diabetes and its impacts, if any, on their diabetes management.Given the recognised importance of social networks in health and wellbeing, the recent rise in popularity of online networking sites affords a timely opportunity to learn more about their role in self-care associated with long-term conditions. Focusing on diabetes as one of the most pressing healthcare priorities, and Facebook as currently the most popular social networking site, the project examines contextual factors that shape knowledge, attitudes and practices in relation to online networking and chronic illness. The Facebook site allows its users to create textual and visual content, connections, organisational and individual pages, and groups, and in this way facilitates maintenance of different network types. Our study of Facebook use by people with diabetes, and by government and third sector organisations, will help us understand the role of these different networks, and of the Internet, in shaping and supporting self-care practices outside formal healthcare organisations. The overall aim is to investigate systematically expert and lay perspectives on online networking and diabetes in the UK through the analysis of discourses and practices surrounding the use of Facebook. Methodologically, the project combines linguistic and sociological approaches and develops a framework for a critical and contextual study of online networking and health. Data was collected through one-to-one semi-structured interviews lasting between 40 and 110 minutes.Interviews were audio recorded. Interviews took place in a variety of settings, including participants' homes, university offices, meeting rooms of Diabetes UK and occasionally in public cafes at the participants' requests. Where interview participants agreed, part of the interview involved accessing and viewing their Facebook account (Newsfeed, profile page, groups) using a laptop or their phone to discuss diabetes-related content that they accessed through Facebook. Unfortunately, the records of this (screenshots, browsing history) are not available through this archive.

Property-Plant-and-Equipment-Gross Time Series for Tandem Diabetes Care Inc. Tandem Diabetes Care, Inc. designs, develops, and commercializes technology solutions for people living with diabetes in the United States and internationally. The company's flagship product is the t:slim X2 insulin delivery system; and Tandem Mobi insulin pump, an automated insulin delivery system. It also sells single-use products, including cartridges for storing and delivering insulin, and infusion sets that connect the insulin pump to the user's body. In addition, the company offers Tandem Device Updater used to update the pump software from a personal computer; Tandem Source, a web-based data management platform, which provides a visual way to display diabetes therapy management data from the pumps, integrated CGMs; and Sugarmate, a mobile app used to help people visualize diabetes therapy data. It has collaboration agreement with the University of Virginia Center for Diabetes Technology for research and development of fully automated closed-loop insulin delivery systems. The company was formerly known as Phluid Inc. and changed its name to Tandem Diabetes Care, Inc. in January 2008. Tandem Diabetes Care, Inc. was incorporated in 2006 and is headquartered in San Diego, California.